A method for evaluating breast cancer screening strategies using screen-preventable loss of life.

The objective of this study is to describe how screen-preventable loss of life (screen-PLL) can be used to analyze the distribution of life savings with mammographic screening. The determination of screen-PLL with mammography is possible using a natural history model of breast cancer that simulates clinical and pathologic events of this disease. This investigation uses a Monte Carlo Markov model with data from the Surveillance, Epidemiology, and End Results Program; American Cancer Society; and National Vital Statistics System. Populations of one million women per screening strategy are simulated over a lifetime with mammographic screening based on current guidelines of the American Cancer Society (ACS), United States Preventive Services Task Force (USPSTF), triennial screening from age 50-70, and no screening. Screen-PLL curves are generated and show guideline performance over a lifetime. The screen-PLL curve with no screening is determined by tumor discovery through clinical awareness and has the highest values of screen-PLL. The ACS and USPSTF strategies demonstrate screen-PLL curves favoring the elderly. The curve for triennial screening is more uniform than the ACS or USPSTF curves but could be improved by adding screen(s) at either end of the 50-70 age range. This study introduces the use of screen-PLL as a tool to improve the understanding of screening guidelines and allowing a more balanced allocation of life savings across an aging population. The method presented shows how screen-PLL can be used to analyze and potentially improve breast cancer screening guidelines.

Insights Into Breast Cancer Screening: A Computer Simulation of Two Contemporary Screening Strategies.

OBJECTIVE: The debate over the value of screening mammography is rekindled with each new published study or guideline. Central to the discussion are the uncertainties about screening benefits and harms and the criteria used to assess them. Today, the magnitude of benefits for a population is less certain, and the evolving concept of harm has come to encompass false-positives (FPs), unnecessary biopsies, overdiagnosis, and overtreatment. This study uses a Monte Carlo computer simulation to study the balance of benefits and harms of mammographic breast cancer screening for average-risk women. MATERIALS AND METHODS: This investigation compares the American Cancer Society's 2015 mixed annual-biennial guideline with the U.S. Preventive Services Task Force's 2016 fixed biennial guideline. Screening strategies are compared using cost-effectiveness acceptability curves, an economic analysis describing uncertainty in evaluating costs and health outcomes. Strategy preference is examined under changing assumptions of willingness to pay for a quality-adjusted life-year. Additionally, comparative effectiveness analysis is performed using FP screens and unnecessary biopsies per life-year gained. Alternative scenarios are compared assuming a reduced mortality benefit of screening. RESULTS: In general, results using both cost-effectiveness and clinical measures indicate that American Cancer Society's 2015 mixed annual-biennial guideline is preferred. Assuming decreases in the mortality benefit of mammography, no screening may be reasonable. CONCLUSION: The use of a mixed annual-biennial strategy for population screening takes advantage of the nonuniformity of occurrence of mammography benefits and harms over the duration of screening. This approach represents a step toward improving guidelines by exploiting age dependencies at which benefits and harms accrue.

Gynecomastia: when is treatment indicated?

This algorithmic approach can simplify your clinical evaluation and help you decide whether intervention or watchful waiting is appropriate.